According to DeepLearning.AI, researchers from Google and Johns Hopkins University have demonstrated that single-embedding retrievers, a widely used AI retrieval method, inherently cannot capture all relevant document combinations as database sizes increase. The study details theoretical limitations linked to embedding size, providing key insights for enterprises relying on vector search technologies. This research sets clearer expectations for retrieval system performance and highlights the need for multi-embedding or agentic approaches to effectively handle complex queries in large-scale AI applications. (Source: DeepLearning.AI, Jan 23, 2026)